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Forecasting of Short Time Series with Intelligent Computing

  • Katarzyna KaczmarekEmail author
  • Olgierd Hryniewicz
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 634)

Abstract

Although time series analysis and forecasting have been studied since the seventeenth century and the literature related to its statistical foundations is extensive, the problem arises when the assumptions underlying statistical modeling are not fulfilled due to the shortness of available data. In such cases, additional expert knowledge is needed to support the forecasting process. The inclusion of prior knowledge may be easily formalized with the Bayesian approach. However, the proper formulation of prior probability distributions is still one of the main challenges for practitioners. Hopefully, intelligent computing can support the formulation of the prior knowledge. In this paper, we review recent trends and challenges of the interdisciplinary research on time series forecasting with the use of intelligent computing, especially fuzzy systems. Then, we propose a method that incorporates fuzzy trends and linguistic summaries for the forecasting of short time series. Experiments show that it is a very promising and human-consistent approach.

Keywords

Time series Forecasting Fuzzy sets Soft computing Bayesian methods 

Notes

Acknowledgments

Katarzyna Kaczmarek is supported by the Foundation for Polish Science under International Ph.D. Projects in Intelligent Computing financed from the European Union within the Innovative Economy Operational Programme 2007–2013 and European Regional Development Fund.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Systems Research Institute, Polish Academy of SciencesWarsawPoland

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